Strategies to Increase Peer Support Specialists’ Capacity to Use Digital Technology in the Era of COVID-19: Pre-Post Study
Bibliographic record
Abstract
BACKGROUND: Prior to the outbreak of coronavirus disease (COVID-19), telemental health to support mental health services was primarily designed for individuals with professional clinical degrees, such as psychologists, psychiatrists, registered nurses, and licensed clinical social workers. For the first the time in history, peer support specialists are offering Medicaid-reimbursable telemental health services during the COVID-19 crisis; however, little effort has been made to train peer support specialists on telehealth practice and delivery. OBJECTIVE: The aim of this study was to explore the impact of the Digital Peer Support Certification on peer support specialists' capacity to use digital peer support technology. METHODS: The Digital Peer Support Certification was co-produced with peer support specialists and included an education and simulation training session, synchronous and asynchronous support services, and audit and feedback. Participants included 9 certified peer support specialists between the ages of 25 and 54 years (mean 39 years) who were employed as peer support specialists for 1 to 11 years (mean 4.25 years) and had access to a work-funded smartphone device and data plan. A pre-post design was implemented to examine the impact of the Digital Peer Support Certification on peer support specialists' capacity to use technology over a 3-month timeframe. Data were collected at baseline, 1 month, 2 months, and 3 months. RESULTS: Overall, an upward trend in peer support specialists' capacity to offer digital peer support occurred during the 3-month certification period. CONCLUSIONS: The Digital Peer Support Certification shows promising evidence of increasing the capacity of peer support specialists to use specific digital peer support technology features. Our findings also highlighted that this capacity was less likely to increase with training alone and that a combinational knowledge translation approach that includes both training and management will be more successful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".